Preliminary and Incomplete
Comments Welcome
When Is Growth Pro-Poor?
Cross-Country Evidence
Aart Kraay*
The World Bank
Abstract: Growth is pro-poor if the poverty measure of interest falls. According to this
definition there are three potential sources of pro-poor growth: (a) a high rate of growth
of average incomes; (b) a high sensitivity of poverty to growth in average incomes; and
(c) a poverty-reducing pattern of growth in relative incomes. This paper empirically
decomposes changes in poverty in a large sample of developing countries during the
1980s and 1990s into these three components. In the medium to long run, most of the
variation in changes in poverty can be attributed to growth in average incomes,
suggesting that policies and institutions that promote broad-based growth should be
central to the pro-poor growth agenda. Most of the remainder of the variation in poverty
is due to poverty-reducing patterns of growth in relative incomes, rather than differences
in the sensitivity of poverty to growth in average incomes. Cross-country evidence
provides relatively little guidance as to the policies and institutions that promote these
other sources of pro-poor growth.
World Bank Policy Research Working Paper 3225, March 2004
The Policy Research Working Paper Series disseminates the findings of work in progress to
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the findings out quickly, even if the presentations are less than fully polished. The papers carry
the names of the authors and should be cited accordingly. The findings, interpretations, and
conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the view of the World Bank, its Executive Directors, or the countries they represent.
Policy Research Working Papers are available online at http://econ.worldbank.org.
*The World Bank, 1818 H Street N.W., Washington, DC, 20433,
akraay@worldbank.org. This paper has been prepared in the context of the pro-poor
growth program sponsored by the World Bank's PREM-Poverty group. I am grateful to
Roberta Gatti, Francisco Ferreira, and Martin Ravallion for helpful discussions, and to
Shaohua Chen for providing data. I would also like to thank the research department at
the International Monetary Fund for its hospitality while parts of this paper were written.
1. Introduction
In the past few years, the term "pro-poor growth" has become pervasive in
discussions of development policy. Despite the widespread use of the term, there
appears to be much less consensus as to what exactly pro-poor growth means, let alone
what its determinants are. According to one view, growth is pro-poor if the
accompanying change in income distribution by itself reduces poverty (Kakwani and
Pernia 2000). However, this definition is rather restrictive, as it implies that, for example,
China's very rapid growth and dramatic poverty reduction during the 1980s and 1990s
was not pro-poor because the poor gained relatively less than the non-poor. A broader
and more intuitive definition is that growth is pro-poor if the poverty measure of interest
falls. Ravallion and Chen (2003) propose this definition and apply it to a particular
poverty measure, the Watts index.
In this paper, I adopt the broader definition, and then apply standard poverty
decomposition techniques to identify three potential sources of pro-poor growth: (a) a
high rate of growth of average incomes; (b) a high sensitivity of poverty to growth in
average incomes; and (c) a poverty-reducing pattern of growth in relative incomes. I
implement this decomposition for several poverty measures, using household survey
data for a large sample of countries in the 1980s and the 1990s. I then use variance
decompositions to summarize the relative importance of these sources of pro-poor
growth. Finally, I investigate the correlates of the sources of pro-poor growth in a large
panel of observations on changes in poverty.
The main results of this paper are the following. First, regarding the relative
importance of the three potential sources of pro-poor growth, I find that roughly half of
the variation in short-run changes in poverty can be explained by growth in average
incomes. In the medium to long run, between 66 and 90 percent of the variation in
changes in poverty can be accounted for by growth in average incomes. Virtually all of
the remainder is due to changes in relative incomes. In contrast, cross-country
differences in the sensitivity of poverty to growth in average incomes account for very
little of the variation in changes in poverty.
1
Second, I find some evidence that growth in average household survey incomes
is correlated with several of the usual determinants of growth from the empirical growth
literature, including institutional quality, openness to international trade, and size of
government. Although the evidence documented here for the correlates of growth in
household survey incomes is not especially compelling, I argue that this likely reflects
the limited country coverage and presence of measurement error in the household
survey data on which this paper is based.
Third, I find relatively little evidence that poverty-reducing patterns of growth in
relative incomes are significantly correlated with a set of explanatory variables that the
empirical growth literature has identified as significant determinants of growth in per
capita GDP. The same is true for a number of other variables, that while not generally
significant for growth, have been suggested in the literature as potentially reducing
inequality.
Taken together, these results underscore the importance of growth in average
incomes for poverty reduction. This in turn suggests that a policy package focusing on
known determinants of growth in average incomes, such as the protection of property
rights, stable macroeconomic policies and openness to international trade should be at
the heart of pro-poor growth strategies. Moreover, the absence of compelling evidence
that these factors are systematically correlated with the changes in income distribution
that matter most for poverty reduction suggests that there are no obvious tradeoffs ş
policies that lead to growth in average incomes are unlikely to systematically result in
adverse effects on poverty through their effects on relative incomes.
This does not mean that growth in average incomes is sufficient for poverty
reduction. Rather, the results presented here suggest that cross-country evidence is
unlikely to be very informative about the policies and institutions that are likely to lead to
poverty-reducing patterns of growth in relative incomes. This suggests that more micro-
level and case-study research may be useful in shedding light on the determinants of
poverty-reducing distributional change.
This paper is related to a growing empirical literature on growth, inequality, and
poverty. Most immediately, this paper builds on Dollar and Kraay (2002). In that paper,
2
we defined the poor as those in the bottom quintile of the income distribution, and
empirically investigated the determinants of growth in incomes of the poorest quintile. In
a large panel of countries, we found that growth in incomes of the poor tracked growth in
average incomes roughly one-for-one. Since the growth rate of average incomes of the
poor is just the sum of the growth rate of average incomes and the growth rate of the
first quintile share, our paper showed that neither average incomes, nor a large set of
other control variables, were significantly correlated with changes in the first quintile
share. That paper contributed to a growing literature on the determinants of inequality,
including Li, Squire and Zhou (1998), Gallup, Radelet and Warner (1998), Spilimbergo,
Londono and Szekely (1999), Leamer, Maul, Rodriguez and Schott (1999), Easterly
(1999), Barro (2000), Foster and Szekely (2001), and Lundberg and Squire (2003).
This paper differs from Dollar and Kraay (2002), as well as much of the existing
literature on determinants of inequality, in two key respects. First, instead of looking at
relative poverty measures or inequality, here I focus primarily on changes in absolute
poverty measures as the dependent variable.1 As is well understood, changes in
absolute poverty measures are complicated and non-linear functions of underlying
changes in average income and income distributions.2 The second contribution of this
paper is to empirically construct the exact measures of distributional change that matter
for changes in poverty for a large sample of countries, rather than simply looking at
common summary statistics of inequality such as the Gini coefficient or quintile shares.
This means that I can empirically study the contributions of growth and distributional
change to changes in poverty, without having to make restrictive assumptions about the
shape of the underlying income distribution.3
Despite these differences, the main conclusions of this paper are similar to those
in Dollar and Kraay (2002). In particular, both papers find that growth in average
incomes matters a great deal for reductions in both relative and absolute poverty. Both
1 A notable early exception is Ravallion and Chen (1997), who estimate regressions of changes in
absolute poverty on changes in mean incomes using a panel of household surveys from
developing countries.
2 See for example Bourguignon (1999) for a lognormal example.
3 For example, Lopez (2003) investigates the determinants of growth and change in the Gini
coefficient, and then draws conclusions regarding the likely effects on poverty by assuming that
the distribution of income is lognormal, so that there is a one-to-one mapping between the Gini
coefficient and the Lorenz curve.
3
papers also find little evidence that common determinants of growth, as well as a
number of other variables, are robustly correlated with patterns of distributional change
that matter for poverty reduction.
The rest of this paper proceeds as follows. Section 2 reviews standard poverty
decomposition techniques and uses them to illustrate the channels through which growth
and distributional change matter for changes in a number of poverty measures. Section
3 describes the dataset of changes in poverty in a large sample of developing countries
on which the empirical analysis is based. Section 4 provides evidence on the relative
importance of the sources of pro-poor growth, as well as evidence on some of the
correlates of these sources. Section 5 concludes.
4
2. Empirical Framework
In this section I use standard techniques to decompose the change in poverty
into three components: (a) growth in average incomes; (b) the sensitivity of poverty to
changes in average incomes; and (c) changes in relative incomes. Let yt(p) denote the
income of the pth percentile of the income distribution at time t. This can be written as a
function of average income, Át, and the Lorenz curve, Lt(p), i.e. yt(p) = Át dLt(p) . Let
dp
Pt denote the following generic additive poverty measure:
1
(1) Pt = f(yt(p)) dp
0
This notation captures a number of different poverty measures. For example, if
f(yt(p),) = z - yt(p)
z up to the headcount,Ht = y-t (z) where z is the poverty line,
1
and zero afterwards, we have the Foster-Greer-Thorbecke class which includes the
headcount (=0), the poverty gap (=1), and the squared poverty gap (=2). If
f(yt(p)) = ln yt(p)
z up to the poverty line and zero afterwards, we have the Watts
poverty index. Another possibility is a broader social welfare function without a
discontinuity at the poverty line, such as Atkinson's (1970) equally-distributed equivalent
1
income (EDEI). In this case f(yt(p)) = yt(p) for all p, and Pt is the poverty measure
of interest.
Next, we can differentiate this poverty measure with respect to time to get:4
4Differentiating under the integral sign in Equation (1) requires the application of Leibnitz's rule.
Note that the term involving the derivative of the upper limit of integration is zero, since the
poverty measures are zero when evaluated at the incomes of those at the poverty line. For EDEI
both the upper and lower limits of integration are constant and so the derivative simply passes
through the integral sign.
5
1
(2) dPt
dt = t(p)gt(p)dp
0
Equation (2) tells us that the rate of change in the poverty measure is the average
across all percentiles of the income distribution of the growth rate of each percentile
multiplied by the sensitivity of the poverty measure to growth in that percentile. In
particular, t(p) df(yt(p))
dyt(p) yt(p) is the semi-elasticity of the poverty measure with
respect to the income of the pth percentile. This term captures the effect on poverty of a
small change in incomes of individuals at the pth percentile of the income distribution.
This sensitivity is multiplied by gt(p) dyt(p) 1
, which captures the growth rate of
dt yt(p)
incomes at each percentile of the income distribution. Ravallion and Chen (2003) refer
to this function as the "growth incidence curve". The overall change in poverty then
consists of the average across all percentiles of the product of these two terms.
In order to separate out the effects of growth in average incomes, we can re-write
Equation (2) by adding and subtracting average growth to get:
(3) dPt dÁt 1 1 1 dÁt dp
1
dt = dt t(p)dp + t(p)gt(p)-
Át dt
0 0 Át
Equation (3) identifies the three sources of pro-poor growth that we have been
discussing: (a) growth in average incomes; (b) the sensitivity of poverty to growth in
average incomes; and (c) growth in relative incomes. The first term in Equation (3)
captures the first two sources of pro-poor growth. It consists of growth in average
incomes, dÁt , multiplied by a term summarizing the sensitivity of the poverty
1
dt Át
1
measure to changes in average incomes, t(p) dp . This sensitivity is simply the
0
average across all percentiles of the sensitivity of poverty to growth in each percentile of
the income distribution. The second term in Equation (3) captures the remaining source
of pro-poor growth: changes in relative incomes. In particular, this third source of pro-
6
pooor growth is the average across all percentiles of the income distribution of the
product of (a) the growth rate of income in the pth percentile relative to average income
growth, and (b) the sensitivity of poverty to growth in that percentile. For example, if the
poverty measure of interest is very sensitive to growth among the poorest, and if the
income of the poorest grows faster than average incomes, then poverty will fall.
Equation (3) is useful for thinking about the various definitions and sources of
pro-poor growth. For example, the Kakwani and Pernia (2000) definition of pro-poor
growth states that growth is pro-poor if and only if the second term in Equation (3) is
negative, i.e. the pattern of growth in relative incomes is such that the poverty measure
falls. A broader definition of pro-poor growth suggested by Ravallion and Chen (2003) is
that growth is pro-poor if the poverty measure of interest falls. According to this
definition, there are three potential sources of pro-poor growth: (a) rapid growth in
average incomes; (b) a high sensitivity of poverty to growth in average incomes; and (c)
a poverty-reducing pattern of growth in relative incomes.
In the empirical section of this paper, I will use data on income distributions and
average incomes for a large sample of developing countries to construct these three
sources of pro-poor growth, document their relative importance, and investigate their
determinants. Before doing so, however, it is useful to examine the key ingredients in
Equation (3) in more detail: the pattern of growth in relative incomes, gt(p) - dÁt ,1
dt Át
and the function summarizing the sensitivity of poverty to growth in each percentile,
t(p).
Figure 1 graphs two examples of the pattern of growth in relative incomes, for
China over the period 1990-1998, and for Indonesia over the period 1996-1999. In
China, according to the household survey average incomes grew at 14 percent per year,
and the dollar-a-day headcount measure of poverty fell from 51 percent to 33 percent of
the population. However, there was also a sharp increase in inequality during this
period, with the Gini coefficient rising from 34 to 40. The pattern of relative income
growth rates shown in the relative growth incidence curve highlights this pattern of
increased inequality. Growth in the poorest 80 percentiles of the population was below
average growth, while the richest 20 percent of the population saw above-average
7
growth. In Indonesia, survey mean income fell dramatically between 1996 and 1999 at
nearly 9 percent per year as a result of the East Asian financial crisis. Yet during this
period, the pattern of growth in relative incomes was poverty-reducing. Inequality as
measured by the Gini coefficient fell from 36.5 to 31.5. The relative growth incidence
curve is downward sloping, indicating that incomes of the richer percentiles of the
income distribution fell faster than incomes of poorer percentiles. In fact, below-average
growth was recorded only for the richest 20 percent of the population. Despite this pro-
poor pattern of relative income growth, the headcount measure of poverty increased
from 8 percent to 13 percent of the population, driven by the large negative growth
effect.
Consider next the sensitivity of the poverty measure to growth in different
percentiles of the income distribution. In the case of the Foster-Greer-Thorbecke class,
t(p) = -(yt(p)/z)(1- yt(p)/z) up to the headcount, and zero afterward. For the
-1
Watts index, t(p) = -1 up to the headcount, and zero afterward. Finally, for EDEI, we
have t(p) = - yt(p) . Note that these sensitivies depend not only on the poverty
measure of interest, but also on the entire distribution of income as summarized by yt(p).
Figure 2 graphs these sensitivities, using the actual distribution of income in China in
1990 as an example, to show how different poverty measures are sensitive to growth in
different percentiles of the income distribution.
In the case of the headcount, this sensitivity is zero everywhere except just below
the poverty line where it spikes down to minus infinity. This is because the headcount
simply adds up the number of people below the poverty line ş small increases in income
of inframarginal poor people that do not bring them above the poverty line will not reduce
the headcount. The same is true for increases in incomes of those above the poverty
line, including the "near-poor" just above the poverty line. The case of the headcount
already illustrates the broader point of Figure 2: whether a given pattern of growth is
pro-poor or not depends crucially on the poverty measure of interest. In particular, if pro-
poor growth in the sense of reducing the headcount measure of poverty is the objective,
then a pro-poor growth strategy should focus exclusively on raising the incomes of those
just at the poverty line, and should ignore everyone else.
8
This strong and slightly absurd conclusion is in part driven by the choice of the
headcount as the poverty measure of interest. Consider next the poverty gap and the
squared poverty gap. The poverty gap is most sensitive to growth in incomes of those at
the poverty line, but is also sensitive to growth in incomes of everyone below the poverty
line. The intuition for this is the following: the poverty gap reflects a social welfare
function which is indifferent to the distribution of income among poor people. In this
case a given rate of average growth results in a larger absolute increase in income for a
person near the poverty line, and so the poverty measure is most sensitive to those
nearest the poverty line, but is non-zero for all poor people.
The squared poverty gap is also sensitive to growth in the incomes of all those
below the poverty line, but the sensitivity is now U-shaped. Growth in incomes of the
richest and poorest of those below the poverty line matters least, and the squared
poverty gap is most sensitive to growth in incomes of poor people somewhere in
between these two extremes. The intuition for this again depends on the underlying
social welfare function, which now values absolute transfers from richer to poorer poor
people. This however is offset by the fact that a given average growth rate results in a
larger absolute increase in income for richer poor people. This is why the sensitivity of
the poverty measure to growth is a non-monotonic function of the income percentile.
The Watts index has the property that it is equally sensitive to growth in all
percentiles below the poverty line. This is why Ravallion and Chen (2003) argue that a
good measure of pro-poor growth is the average (across all percentiles) growth rate of
those below the poverty line, i.e. the average growth rate of incomes of the poor. In this
paper I go further and decompose the average growth rate of incomes of the poor into
growth in average incomes and the average growth rate of the poor relative to growth in
average incomes. This allows me to distinguish between the effects of growth in
average incomes and growth in relative incomes on the Watts measure, and all the other
measures considered here. This distinction is not trivial, as we will see in the empirical
section of the paper that there is more evidence for the correlates of growth in average
incomes than growth in relative incomes.
Finally, when inequality aversion is positive, i.e. <1, the EDEI measure is most
sensitive to growth in incomes of the poorest, but is non-zero for all income percentiles.
9
The key difference with the other poverty measures is that there is no longer a
discontinuity at the poverty line ş growth in all parts of the income distribution matters for
poverty reduction, with growth among the poorest mattering most.
To reiterate, the important point of Figure 2 is that poverty measures differ in their
sensitivity to growth in different percentiles of the income distribution. As a result, a
given pattern of relative income growth might be pro-poor (in the sense that the poverty
measure falls) for some poverty measures, but not for others. Moreover, if we take
seriously the objective of pro-poor growth with respect to a particular poverty measure,
then this requires a growth strategy focusing on particular parts of the income
distribution. For example, pro-poor growth with respect to the headcount requires an
emphasis on those just below the headcount, while pro-poor growth with respect to EDEI
with strong inequality-aversion requires interventions targetted to reaching the poorest of
the poor.
Finally consider the average across all percentiles of the sensitivity t(p) of
poverty to growth in incomes of percentile p. Recall from Equation (3) that this average
sensitivity measures the effect of growth in average incomes on the poverty measure.
We have been referring to high values of this average sensitivity of poverty to growth in
average incomes as one of the three potential sources of pro-poor growth. For the
Foster-Greer-Thorbecke class of poverty measures, this average sensitivity can be
expressed in terms of the poverty measure itself when is not equal to zero,
1
t(p) dp=- (Pt()-Pt( -1)), where Pt() denotes the FGT measure with parameter .5
0
In the case where is zero, the sensitivity of the headcount to growth in average
1
incomes is: t(p) dp=-
Lt '(H) which can be expressed as the slope of the density
0 ÁLt''(H)
of income at the poverty line. For the Watts measure, the average elasticity is simply
minus one times the headcount. For EDEI, the sensitivity to average growth is EDEI
itself, implying that the elasticity of EDEI with respect to growth in average incomes is
one. While these results are useful for analytically characterizing the sensitivity of the
different poverty measures to growth in average incomes, we will see shortly that cross-
5 This result can be found in Kakwani (1993).
10
country differences in the sensitivity of poverty to growth in average incomes are not
empirically very important, in the sense that they explain little of the cross-country
variation in the first term in Equation (3). We therefore do not discuss them further here.
11
3. Data
The objective of the rest of this paper is to use the analytic framework discussed
above to decompose observed changes in poverty into the three terms discussed above:
(a) growth in average incomes; (b) the sensitivity of poverty to growth in average
incomes; and (c) changes in relative incomes. After constructing these three terms for a
large sample of developing countries, I use them to identify the relative importance of,
and factors correlated with, these various sources of pro-poor growth.
I use household survey data on average incomes and 10 points on the Lorenz curve
for a large number of surveys, as compiled by Martin Ravallion and Shaohua Chen at
the World Bank. Their data come directly from primary sources, have been meticulously
cleaned, and are available at http://www.worldbank.org/research/povmonitor .6
Depending on the country, the surveys measure either the distribution of income or the
distribution of consumption. Average income or consumption is measured in 1993
dollars and is adjusted for cross-country differences in purchasing power parity. Since I
am interested in changes in poverty over time, I take only countries with at least two
household surveys. This results in a total of 285 surveys covering 80 developing
countries. Most of the survey dates are in the 1990s, with some countries extending
back to the 1980s. I use the World Bank's "dollar-a-day" poverty line, which in 1993
dollars is $1.08 per day, or $393 per year.
Using these surveys, I construct two datasets of spells of changes in poverty. In the
first dataset, I consider all possible spells for each country, discarding only those few
cases where the survey changes from an income to an expenditure survey or vice versa.
This results in 205 spells of poverty changes. The length of these spells is quite short,
averaging 3.5 years and ranging from one to 13 years. In order to be able to look at
changes over longer horizons, I also construct a dataset consisting of one spell per
country, where the initial and final years are chosen so as to maximize the length of the
spell given available data. This results in a set of 80 spells, with an average length of
8.2 years, and ranging from two to 19 years. Finally I eliminate all spells where the
headcount measure of poverty is negligible in either the initial or final period, i.e. below 2
6 I am grateful to Shaohua Chen for kindly providing key data from all of the household surveys,
including some that are not available on the poverty monitoring website.
12
percent, and I also drop a number of spells where the average annual growth rate in the
survey mean is implausibly large, i.e. more than 15 percent in absolute value. This
reduces the first dataset to 128 spells covering 58 countries with an average length of
3.5 years, and the second dataset to 42 spells with an average length of 9.6 years.
In order to construct the poverty measures and their decompositions discussed in
the previous section, I need the full Lorenz curve and not just the 10 points provided in
the Ravallion-Chen data. To obtain this, I assume that the Lorenz curve has the
following functional form:
(4) L(p) = p 1- (1- p) , 0, 0 < 1, 1
( )
This particular parameterization is a member of a family of ordered Lorenz curves
proposed by Sarabia, Castillo, and Slottje (1999). I estimate the parameters of this
Lorenz curve for each survey using an algorithm suggested by the same authors. This
involves selecting all possible combinations of three points on the Lorenz curve, and
then for each combination finding values of , , and such that the Lorenz curve
passes through these three points. The final estimates of , , and are then found by
averaging across all the resulting estimates of these parameters, discarding those for
which the parameter restrictions indicated in Equation (4) that are required for the
Lorenz curve to have positive first and second derivatives do not hold. I then obtain the
quantile function by analytically differentiating the Lorenz curve and multiplying by
average income. Using this, I can immediately construct t(p) for each poverty measure
of interest, as well as the growth incidence curve over the observed discrete interval,
gt(p) = yt(p) -1.
yt (p)
-1
13
4. Results
I begin by constructing the poverty measures of interest (the headcount, the
poverty gap, the squared poverty gap, the Watts index) for the initial and final years of
each spell. I then compute average annual changes in these measures, normalizing
each by its initial value so as to get proportionate changes that are more easily
comparable across poverty measures. Table 1 reports the simple correlations of the
levels and average annual growth rates in these poverty measures with the
corresponding log-levels and growth rates of survey mean income. These simple
correlations are all negative, and are large in absolute value, especially those in levels
and those for the long spells. Figure 3 graphs the proportional change in each poverty
measure against the growth rate of average incomes, using the sample of long spells. In
each case, there is a strong and highly significant negative relationship between
changes in poverty and changes in average incomes. There is somewhat more
dispersion around this average relationship for the more bottom-sensitive poverty
measures such as the poverty gap and the squared poverty gap, than for the headcount
measure. However, this may simply reflect the greater sensitivity of bottom-sensitive
poverty measures to measurement error in individual incomes, as I argue in more detail
below.
Table 1 and Figure 3 confirm the widely-understood empirical regularity that
poverty measures tend to fall as average incomes increase. The rest of this section
documents the relative importance of the different sources of pro-poor growth discussed
above, and some evidence on the correlates of growth in average and relative incomes.
Relative Importance of Sources of Pro-Poor Growth
I now document the relative importance of the three sources of pro-poor growth
that we have been discussing. I do this in two steps. I first decompose the change in
poverty in each spell into a "growth component" and a "distribution component" using the
decomposition suggested by Datt and Ravallion (1992), which is the discrete-time
analog of Equation (3). Let P(Át ,Lt ) denote a poverty measure based on mean income
at time t, Át, and the Lorenz curve at time t, Lt. I then write the proportional change in the
poverty measure over the discrete interval between time t and t-1 as:
14
(5) P(Át ,Lt )- P(Át-1,Lt-1) P(Át ,Lt-1)- P(Át-1,Lt-1) P(Át-1,Lt )- P(Át-1,Lt-1)
P(Át-1,Lt-1) = P(Át-1,Lt-1) + P(Át-1,Lt-1) + t
The first term on the right-had side is the growth component of the change in poverty,
and is constructed as the proportional difference between the initial poverty measure
and a hypothetical poverty measure computed using the second period mean but the
first period Lorenz curve. The second term is the distribution component which is
computed as the proportional difference between the initial poverty measure and a
hypothetical poverty measure constructed using the first period mean but the second
period Lorenz curve. These two components are the discrete-time analogs of the two
terms in Equation (3). Unlike Equation (3), however, there is also a residual term
because the decomposition is done over a discrete and not an infinitesmal interval. I
measure the proportional changes on the left- and right-hand side of Equation (5) as log
differences and normalize by the length of the interval to get average annual percent
changes in poverty and its growth and distribution components for each spell.
Tables 2 and 3 report the results of applying this decomposition to the two
datasets of spells. Throughout these two tables, I use the following variance
decomposition to summarize the relative importance of the various components. If X
and Y are two correlated random variables, then I define the share of the variance of
VAR(X)+ COV(X,Y)
X+Y due to variation in X as .7 The top panel of each
VAR(X)+ VAR(Y)+2COV(X,Y)
table documents the importance of the residual relative to the sum of the growth and
distribution components of the change in poverty. The first column shows the variance
of the sum of the growth and distribution components, the second column the variance
of the residual, and the third the covariance between the two. The final column reports
the share of the variance of changes in poverty due to the growth and distribution
components, which is virtually one for all poverty measures. This simply reflects the fact
that the variance of the residual term is tiny relative to the variance in measured changes
in poverty. This can also be verified visually from the top panel of Figure 4, which
7 When X and Y are normally distributed, this variance decomposition has a very natural
interpretation. It tells us how much the conditional expectation of X increases for each unit that
we observe the sum (X+Y) to be above its mean value.
15
graphs the change in poverty on the horizontal axis, and the sum of the growth and
distribution components on the vertical axis, using the dataset of long spells. The slope
of the OLS regression line is the share of the variance in poverty changes due to the
growth and distribution components, and one minus the slope is the share due to the
residual term. It is clear from this graph that changes in poverty are largely accounted
for by the sum of the growth and distribution components, with very little of the variation
due to the residual.
The middle panel of Tables 2 and 3 does the same variance decomposition, but
now to assess the importance of the growth component relative to the distribution
component of changes in poverty. For the sample of all spells, between one-third and
one-half of the variation in changes in poverty is due to the growth component, with the
remainder due to changes in distribution. The story is quite different for the long spells,
where the growth component of changes in poverty dominates, accounting for between
65 and 90 percent of changes in poverty. In both tables, the growth component is
relatively less important for bottom-sensitive poverty measures such as the poverty gap
and the squared poverty gap. The middle panel in Figure 4 graphically summarizes this
second decomposition for the long spells sample, plotting the growth component of
changes in poverty on the vertical axis, and the sum of the growth and distribution
components on the horizontal axis. Again, the slope of the OLS regression line can be
interpreted as the share of the variation on the horizontal axis due to the growth
component. Visually inspecting this graph, it is clear that if poverty reduction is large, it
is mostly because the growth component of poverty reduction is large.
The bottom panel of Tables 2 and 3 further disentangles the growth component
into growth in average incomes, and the sensitivity of poverty to growth in average
incomes, i.e. it separates the first term in Equation (3) into its two components. Since
the decomposition we have been using applies to sums of random variables, I take the
logarithm of the absolute value of the growth component, which then becomes the sum
of the logarithm of the absolute value of growth, and the logarithm of the absolute value
of the average sensitivity of poverty to growth, and apply the decomposition to this sum.
Tables 2 and 3 show that over 80 percent of the cross-country variation in the growth
component of changes in poverty is due to cross-country differences in average income
growth, and very little is due to cross-country differences in the sensitivity of poverty to
16
average income growth. The bottom panel of Figure 4 illustrates this, but without the log
transform required to do the variance decomposition. On the horizontal axis I graph the
growth component of the change in poverty, while on the vertical axis I graph growth in
average incomes. While the slope of this regression cannot be interpreted as a variance
share, it nevertheless is very clear that cross-country differences in the growth
component of poverty are overwhelmingly accounted for by cross-country differences in
growth. Put differently, it is clear from this graph that if the growth component of poverty
reduction is large, it is most likely that growth itself was large, rather than that the
sensitivity of poverty to growth was large.8
Two striking features of Tables 2 and 3 merit further discussion: (a) the share of
the variance due to growth is smaller over the short horizons represented in the dataset
of all spells, and is larger in the dataset of long spells; and (b) in both datasets, the share
of the variation in poverty measures due to growth declines as the poverty measures
become more bottom-sensitive, for example when we move from the headcount to the
poverty gap to the squared poverty gap. We can understand these properties better with
the help of a simple example using the EDEI poverty measure. We can write the
discrete proportional change in EDEI as:
(6) lnEDEIt()=lnEDEIt(1)+(lnEDEIt()-lnEDEIt(1))
Recall that EDEI(1) is just average income, and that the sensitivity of EDEI to growth in
average incomes is one. As a result, Equation (6) is a way of writing the Datt-Ravallion
decomposition for this measure, with the first term corresponding to the growth
component and the second to the distribution component. Moreover, the distributional
change component of the change in this poverty measure corresponds to the change in
8At first glance this result seems inconsistent with Ravallion (1997), who documents that the
sensitivity of poverty to growth varies significantly with initial inequality. However, using either
sample of spells I can replicate the result that the interaction of growth with the initial Gini
coefficient is significantly correlated with the change in headcount measures of poverty.
Intuitively, the difference between the results here and those in Ravallion (1997) can be
understood as follows: although the interaction of growth with initial inequality is significant in
explaining changes in poverty, it does not add much to the explanatory power of the regression in
my samples. Put differently, although there are cross-country differences in the sensitivity of
poverty to growth which are significantly correlated with initial inequality, in the data these
differences are dominated by the much larger cross-country differences in growth itself.
17
a particular inequality measure: it is simply the proportional change in one minus the
Atkinson inequality measure.9
For the purpose of this example, assume that the logarithm of household
incomes is distributed normally with mean Át and standard deviation t. As the number
of households in each country becomes large, it is straightforward to see that lnEDEI()
converges in probability to Á + 2 .10 Using this result, we can write the Datt-Ravallion
2
decomposition for EDEI as:
(7) lnEDEIt() Át + p -1 2 2t
Equation (7) is helpful for understanding the two key features of Tables 2 and 3
mentioned above. Suppose that Át and 2t are independent across countries. Then
the share of variance of changes in poverty due to growth with be
V Át /V Át +
[ ] [ ] -1 [ ]
2
. The further is from one, i.e. the more EDEI
2 V 2t
weights incomes of the poor (for <1) or the rich (for >1), the smaller is the share of the
variance of change in poverty due to growth and the larger is the share due to the
distribution component. In other words, the more bottom-sensitive (or for that matter,
top-sensitive), the poverty measure, the larger will be the contribution of changes in
relative incomes to changes in the poverty measure. This suggests an explaination why
the share of the variance of changes in poverty due to growth declines as the poverty
measures become more bottom-sensitive.
Equation (7) is also helpful for thinking about why the share of the variance of
changes in all poverty measures due to growth is smaller in the short run than in the long
run. One possible explanation is that measurement error in changes in inequality is
9The Atkinson class of inequality measures is 1-EDEI()/EDEI(1).
10This is because EDEI()1/ is the sample average of incomes raised to the power . As the
number of households becomes large, this converges to the expectation of income raised to the
power . If incomes are lognormally distributed, we can use the moment generating function of
the lognormal distribution to evaluate this expectation to obtain the result in the text.
18
relatively more important than measurement error in changes in average incomes when
the period under consideration is short. It is not clear how one might directly document
that this is the case. However, it is worth noting that this pattern of relative importance of
measurement error seems quite plausible. Suppose for example that in every period,
log household income is measured with an additive zero-mean measurement error,
which is independent of true incomes and is i.i.d. normal across households. If the
number of households is large, this zero-mean measurement error will not be reflected in
average income. However, the variance of measured log incomes will now be 2t + 2t ,
where 2t is the variance of measurement error. Suppose further that the variance of
measurement error fluctuates randomly over time. As long as the the variance of
measurement error does not trend up or down too fast, the average annual change in
the distribution component of changes in poverty, -12t -k2t-k + 2t - 2t-k will be
2 k
smaller the longer is the time interval, k. While this is not conclusive, it does suggest
that part of the reason for the relatively smaller importance of the growth component of
changes in poverty over shorter horizons might simply be measurement error in
household incomes.
In summary, the results in this subsection tell us that, over longer horizons,
between 65 and 90 percent of cross-country differences in poverty changes can be
accounted for by growth in average incomes. Over shorter horizons the share of the
variance of changes in poverty due to changes in growth is somewhat smaller, and
changes in income distribution are relatively more important. However, this may in part
be an artifact of measurement error in individual incomes. While there are of course
cross-country differences in the sensitivity of poverty changes to average income
growth, reflecting cross-country differences in the initial distribution of income,
empirically these are relatively unimportant in understanding changes in poverty.
Finally, although these calculations are done based on a discrete-time decomposition
with unavoidable residuals, empirically these residuals are also small and do not detract
from the main conclusions.
19
What Drives the Sources of Pro-Poor Growth?
I now turn to the question of what drives the various sources of pro-poor growth.
In light of the results of the previous section that cross-country differences in the
sensitivity of poverty to growth in average incomes are relatively unimportant, I focus
primarily on the first and third sources of pro-poor growth: growth in average incomes,
and changes in relative incomes. I measure growth in average incomes as the average
annual growth rate over the spell of household average income or consumption. I use
five different measures of changes in relative incomes. The first is simply the average
annual change in the Gini index, for comparability with existing results on the
determinants of changes in inequality. The next four measures are the discrete-time
distribution components of the change in each of the four poverty measures I have been
considering. Recall that, for infinitesmal changes, the distribution component of the
change in the headcount measures the growth rate of incomes of those at the poverty
line relative to average growth. For the poverty gap and the squared poverty gap, the
distribution component measures a weighted average of relative growth rates of those
below the poverty line, with the poverty gap giving most weight to those at the poverty
line. For the Watts index, the distribution component measures the average growth rate
of those below the poverty line relative to overall growth.
There are many limitations to this dataset which make it very difficult to use it to
identify causal determinants of growth or change in relative incomes. The sample of
countries is quite small, especially when we consider the long spells dataset where the
determinants of longer-term growth and distributional change are more likely to be
apparent. There is also substantial measurement error in the data on growth in survey
means, and for measures of distributional change. While classical measurement error in
these dependent variables will not necessarily lead to biases in coefficient estimates, it
will inflate standard errors and reduce the significance of estimated coefficients.
Because we have relatively few spells per country in the dataset consisting of all spells,
and only one per country in the long spells dataset, we cannot meaningfully base
identification on the within-country variation in the data. This raises the possibility that
any partial correlations we uncover may be driven by unobserved country-specific
characteristics excluded from the regressions. The small number of spells per country
20
also means that we will not be able to rely on internal instruments to achieve
identification.11
In light of these difficulties, my more modest objective here is to simply
document the partial correlations between these sources of pro-poor growth and a
number of right-hand-side variables of interest, and to interpret them with an appropriate
abundance of caution. I consider the same list of right-hand-side variables as in Dollar
and Kraay (2002). In that paper, we considered a small number of variables that are
frequently found to be robustly correlated with real GDP growth in the cross-country
growth literature: institutional quality as proxied by a measure of property rights
protection (the "rule of law" indicator from Kaufmann, Kraay and Mastruzzi (2003)) as
well as the World Bank's Country Policy and Institutional Assessment (CPIA) indicator;
openness to international trade (the constant-price local currency ratio of exports plus
imports to GDP); inflation as a proxy for stable monetary policy (measured as the
logarithm of one plus the CPI inflation rate); the size of government (measured as the
share of government consumption in GDP in local currency units); and a measure of
financial development (the ratio of M2 to GDP in local currency units).
We also considered a number of variables that are generally less robustly
correlated with growth, but that some studies have found to be correlated with inequality,
either in levels or in differences. These include a measure of democracy (the "voice and
accountability" indicator from Kaufmann, Kraay and Mastruzzi (2003)); relative
productivity in agriculture (measured as the ratio of value added per worker in agriculture
relative to overall value added per worker, both in current local currency units); and
primary educational attainment.
This list of variables is clearly not an exhaustive list of the potential determinants
of growth in average incomes or changes in relative incomes. However, it does provide
us with a useful place to begin looking for the correlates of growth and distributional
change that matter for poverty reduction. I begin by estimating a number of very
11This is of course especially problematic for the regressions below that involve a lagged
dependent variable, which, together with unobserved country-specific effects, will make estimates
of the coefficient on the lagged dependent variable inconsistent, and can bias the coefficients on
the other variables in different directions depending on their correlation with the lagged
dependent variable.
21
parsimonious regressions for each of the dependent variables of interest. I regress
growth in average incomes on the log-level of initial period income (to pick up
convergence effects) plus each of the control variables described above, one at a time. I
do the same for the change in the Gini coefficient, instead including the initial level of the
Gini coefficient to pick up convergence in this variable. For the remaining four
distribution components of changes in poverty, I simply estimate univariate regressions
of each one on each of the right-hand-side variables. 12
Table 4 shows the results using the sample of all spells, and Table 5 shows the
same information but using only the smaller sample of long spells. Each entry in these
two tables corresponds to a different regression. The rows correspond to each of the
indicated right-hand-side variables. The columns correspond to the different dependent
variables. The first two columns report regressions for growth and for the change in the
Gini. Both these regressions also include either initial log income or the initial Gini. I do
not report the coefficients on these variables to save space, but they generally enter
negatively and usually significantly in all specificiations, consistent with available
evidence on convergence in both of these variables. The remaining columns report
results for the distribution component of the change in each of the four poverty
measures. Recall that these measures are oriented such that a reduction corresponds
to a reduction in poverty.
A first glance at Tables 4 and 5 show that very few of the explanatory variables of
interest are significantly correlated with the dependent variable of interest at
conventional significance levels. In fact, in the 108 regressions in these two tables,
there is only one coefficient that is significant at the 5 percent level, and only three that
are significant at the 10 percent level. One possible explanation for the lack of
significant results is that the measures of growth and distributional change on the right-
hand-side are contaminated by substantial measurement error. It is difficult to judge
however by how much standard errors should be adjusted to reflect this measurement
12Ravallion (2001) documents the empirical importance of inequality convergence using the Gini
coefficient. I have experimented with alternative initial inequality measures in the regressions
involving the distributional change components of the various poverty measures, but I find that
none are robustly significant.
22
error.13 Rather than try to assess the statistical significance of the partial correlations
documented in Tables 4 and 5, I simply describe some of the qualitative patterns that
emerge.
Consider first institutional quality, as proxied by the rule of law indicator. This
tends to be positively correlated with growth, but also positively correlated with each of
the measures of distributional change, suggesting that distributional change tends to
raise poverty in countries with good institutional quality. However, the strength of the
correlation with growth is much larger than the correlations with distributional change:
the t-statistic from the growth regression is about twice the average t-statistic for the
different measures of distributional change. The voice and accountability measure
follows the same pattern, likely because it is quite highly correlated with rule of law in
this sample.
In the case of openess to international trade, the correlation with growth is
generally stronger than the correlations with distributional change. Moreover, the sign of
the correlation with each of the measures of distributional change is negative, indicating
that distributional change tends to be poverty-reducing in countries that trade more.
Inflation tends to be extremely weakly correlated with growth in this sample, and tends to
be positively correlated with distributional change, but again the correlation is very weak.
Government consumption is negatively correlated with growth, but interestingly is also
negatively correlated with each of the measures of distributional change, suggesting that
distributional change tends to be pro-poor in countries with larger governments.
Financial development also appears to be very weakly correlated with either growth or
distributional change in these regressions.
Relative productivity in agriculture is essentially uncorrelated with growth, but
tends to be positively correlated with distributional change measures. Somewhat
surprisingly the sign of the correlation suggests that countries with higher relative
productivity in agriculture are more likely to experience poverty-increasing changes in
relative incomes. Finally, primary education is also virtually uncorrelated with growth,
13In Table 4, there is an additional factor which likely biases standard errors upward. For
countries with multiple spells of growth or distributional change, there is likely to be by
construction a negative correlation between the errors of successive spells. Correcting for this
will likely reduce standard errors somewhat.
23
and also is essentially uncorrelated with most of the distributional change measures,
with the exception of the Gini in the long spells regression.
Overall, while most of the partial correlations documented in Tables 4 and 5 are
not statistically significant, the qualitative pattern suggests that there may be some
tradeoffs. Rule of law is positively correlated with growth but also with poverty-
increasing shifts in relative incomes. The opposite is true for government consumption.
In contrast trade is positively correlated with growth and with poverty-reducing shifts in
relative incomes. In Table 6 we examine these possible tradeoffs in a slightly richer
empirical specification, using the dataset of long spells. We begin by estimating a more
fully-specified growth regression with initial income, and initial values of institutional
quality, trade openness, and size of government as right-hand-side variables. Despite
the likely noisiness of the data, it is possible to find plausible specifications in which
some of the determinants of growth from the growth literature are also significantly
correlated with growth in the household survey mean. The first column of Table 6
illustrates one such regression, which includes initial income, institutional quality, trade
openness, and government consumption on the right-hand-side. Each of these
variables enters with signs consistent with the broader growth literature. Initial income
enters negatively, picking up convergence effects. Institutional quality and trade are
both positively correlated with growth, and larger government size is associated with
slower growth. I do not want to claim that these results are a robust feature of this
particular dataset. However, the results are broadly consistent with the findings of the
empirical growth literature, which uses per capita GDP growth rates for a much larger
sample of countries, and so it seems reasonable to focus on this particular specification.
In the second column of Table 6, I show the same regression, but instead using
the change in the Gini coefficient as the dependent variable. None of the correlates of
growth are significantly correlated with changes in this summary statistic of inequality. It
is however difficult to move from the results in these first two columns to conclusions
about the effects on poverty, without making restrictive assumptions on the shape of
income distributions. Since I have already constructed the growth and distribution
components of changes in poverty, I can simply use these as dependent variables to
investigate how these correlates of growth matter for changes in poverty. The remaining
four columns of Table 6 do this for the headcount and for the Watts index. Given the
24
high correlation between the growth components of poverty changes and average
income growth documented above, it is not surprising that the regressions for the growth
components of poverty are very similar to the growth regression in the first column.
Institutional quality, trade, and government size are significantly correlated with the
growth components of changes in these two poverty measures, although the
significance is slightly less than before. In contrast, I find very little evidence that any of
these three variables are significantly correlated with the distribution component of
changes in poverty. The only exception is institutional quality, which is significant at the
10 percent level in the headcount distribution component regression. The sign indicates
that poverty-increasing distributional change is more likely to occur in countries with
better institutional quality.
Despite the general insignificance of the distributional change regressions, it is
interesting to quantify the relative magnitudes of the estimated coefficients as well.
Since the observed change in poverty is essentially equal to the sum of the growth and
distribution components (with a relatively unimportant residual as we have seen), the
overall effect on poverty of each of these variables is just the sum of the two coefficients.
Figure 5 graphically illustrates the growth and distribution effects of these variables on
poverty as measured by the headcount. For Rule of Law, the growth effect lowers
poverty, while the distribution effect raises it. The overall net effect is negative, however,
since the growth effect is larger in absolute value than the distribution effect. For trade,
both the growth and distribution effects reduce poverty, with a much larger growth effect.
Finally, the growth and distribution effects work in opposite directions for government
size, again with the adverse growth effect dominating the smaller poverty-reducing
distribution effect.
25
Conclusions
What do we learn from all of this? I have used standard decomposition
techniques to identify three potential sources of pro-poor growth: (a) a high rate of
growth of average incomes; (b) a high sensitivity of poverty to growth in average
incomes; and (c) a poverty-reducing pattern of growth in relative incomes. Empirically
implementing these decompositions for a large sample of changes in poverty, we have
seen that only the first and third sources of pro-poor growth are empirically relevant.
Moreover, in the medium to long run, cross-country differences in growth in average
incomes are the dominant factor explaining changes in poverty. Together, these
decomposition results indicate that the search for pro-poor growth should begin by
focusing on determinants of growth in average incomes. At some level, this is an
encouraging conclusion, because we have by now a large body of empirical results on
the policies and institutions that drive growth in average incomes.
Nevertheless, the empirical results shown here on the correlates of growth and
distributional change are rather unsatisfactory. Most of the simple correlations between
these dependent variables and a number of right-hand-side variables of interest are
quite far from significant at conventional levels, although the balance of the evidence
seems to suggest that the correlations with growth are on average somewhat more
significant than the correlations with distributional change. It is possible to find
multivariate specifications for growth in survey means over longer horizons that yield
sensible results consistent with the empirical growth literature. At most, this provides
some comfort that the results on partial correlates of growth in survey mean income
documented here are more broadly robust and may even have causal interpretations.
However, there is much more to be learned about why per capita GDP growth, whose
determinants are well-documented, translates so imperfectly into growth in survey
means.14
In contrast, in this sample it is difficult to find significant correlates of either
changes in summary statistics of inequality such as the Gini, or distributional shifts that
matter for a variety of poverty measures of interest such as the ones I have constructed
here. Moreover, some of the partial correlations with distributional change documented
14See for example Deaton (2003) for a discussion of some of the relevant issues.
26
here do not appear to be consistent with those uncovered in other papers. For example,
a number of papers have found that increased openness increases summary measures
of inequality, at least in low-income countries (Barro (2000), Lundberg and Squire
(2003), Milanovic (2003)). In contrast Dollar and Kraay (2002) find no correlation at all
between several measures of openness and income distribution, with and without
several interactions. The results here, although not very significant, consistently show
that more open countries are more likely to see poverty-reducing shifts in income
distribution. This wide range of signs and significance of results from the cross-country
literature should caution us against drawing particularly strong conclusions about the
determinants of pro-poor changes in relative incomes from any one cross-country study.
27
References
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29
Table 1: Correlations of Poverty Measures and
Survey Mean Income or Consumption
Levels Growth Rates
All Spells (128 Observations)
Headcount -0.842 -0.590
Poverty Gap -0.727 -0.519
Squared Poverty Gap -0.615 -0.472
Watts -0.647 -0.489
Long Spells (42 Observations)
Headcount -0.935 -0.717
Poverty Gap -0.722 -0.672
Squared Poverty Gap -0.630 -0.635
Watts -0.651 -0.640
30
Table 2: Decomposing Changes in Poverty: All Spells
Growth, Distribution and Residual Components of Change in Poverty: dP = G + D + R
Growth and Distribution Components vs Residual (G+D vs R)
V(G+D) V(R) COV(G+D,R) Share of Variance Due to G+D
Headcount 0.0284 0.0008 -0.0004 0.9859
Poverty Gap 0.0485 0.0010 -0.0004 0.9877
Squared Poverty Gap 0.0702 0.0013 -0.0007 0.9914
Watts 0.0599 0.0010 -0.0005 0.9917
Growth vs Distribution Components (G vs D)
V(G) V(D) COV(G,D) Share of Variance Due to G
Headcount 0.0168 0.0173 -0.0029 0.4912
Poverty Gap 0.0216 0.0334 -0.0033 0.3781
Squared Poverty Gap 0.0255 0.0517 -0.0035 0.3134
Watts 0.0231 0.0435 -0.0033 0.3300
Average Growth and Sensitivity to Average Growth in Growth Component: ln|G| = ln|dlnÁ| + ln||
V(|dlnÁ|) V(||) COV(|dlnÁ|,||) Share of Variance Due to |dlnÁ|
Headcount 1.1290 0.1577 0.1436 0.8086
Poverty Gap 1.1089 0.1311 0.1202 0.8302
Squared Poverty Gap 1.1073 0.1369 0.1224 0.8259
Watts 1.1021 0.1243 0.1135 0.8364
31
Table 3: Decomposing Changes in Poverty: Long Spells
Growth, Distribution and Residual Components of Change in Poverty: dP = G + D + R
Growth and Distribution Components vs Residual (G+D vs R)
V(G+D) V(R) COV(G+D,R) Share of Variance Due to G+D
Headcount 0.0064 0.0005 -0.0004 0.9836
Poverty Gap 0.0120 0.0008 -0.0010 1.0185
Squared Poverty Gap 0.0181 0.0011 -0.0017 1.0380
Watts 0.0148 0.0009 -0.0011 1.0148
Growth vs Distribution Components (G vs D)
V(G) V(D) COV(G,D) Share of Variance Due to G
Headcount 0.0077 0.0024 -0.0018 0.9077
Poverty Gap 0.0106 0.0046 -0.0016 0.7500
Squared Poverty Gap 0.0129 0.0073 -0.0010 0.6538
Watts 0.0116 0.0063 -0.0015 0.6779
Average Growth and Sensitivity to Average Growth in Growth Component: ln|G| = ln|dlnÁ| + ln||
V(|dlnÁ|) V(||) COV(|dlnÁ|,||) Share of Variance Due to |dlnÁ|
Headcount 1.3228 0.1642 0.1402 0.8278
Poverty Gap 1.2836 0.1492 0.1131 0.8419
Squared Poverty Gap 1.2765 0.1527 0.1114 0.8401
Watts 1.2702 0.1414 0.1025 0.8491
32
Table 4: Correlates of Pro-Poor Growth: All Spells
Dependent Variable Is:
Distribution Component of Change in:
Growth Change in Gini P0 P1 P2 Watts # Obs
RHS Variable is:
CPIA 0.006 -0.001 -0.007 -0.007 -0.008 -0.009 121
0.920 0.160 0.477 0.354 0.310 0.360
KK Rule of Law 0.012 0.002 0.016 0.019 0.023 0.019 128
1.339 0.292 0.766 0.674 0.633 0.568
Trade/GDP 0.017 -0.017 -0.034 -0.038 -0.041 -0.041 126
1.120 1.431 0.990 0.805 0.692 0.757
ln(1+Inflation) 0.001 0.006 0.039 0.039 0.039 0.037 116
0.059 0.323 0.790 0.566 0.449 0.474
Government -0.154 -0.053 -0.421 -0.488 -0.507 -0.532 125
Consumption/GDP 1.206 0.515 1.539 1.250 1.041 1.192
M2/GDP 0.015 -0.008 -0.024 -0.013 -0.002 -0.010 124
0.638 0.431 0.475 0.182 0.028 0.130
KK Voice and 0.002 0.006 0.003 0.004 0.004 0.002 128
Accountability 0.294 0.891 0.194 0.150 0.153 0.086
Relative Productivity -0.005 0.017 0.053 0.060 0.065 0.061 126
in Agriculture 0.271 1.200 1.324 1.106 0.969 0.994
Average Years of 0.003 0.002 -0.006 -0.007 -0.008 -0.010 101
Primary Education 0.538 0.649 0.534 0.477 0.410 0.576
Note: Heteroskedasticity-consistent t-statistics reported below coefficient estimates.
33
Table 5: Correlates of Pro-Poor Growth: Long Spells
Dependent Variable Is:
Distribution Component of Change in:
Growth Change in Gini P0 P1 P2 Watts # Obs
RHS Variable is:
CPIA 0.005 0.001 0.001 0.004 0.006 0.005 40
0.555 0.267 0.143 0.282 0.336 0.315
KK Rule of Law 0.024 0.007 0.022 0.022 0.021 0.020 42
1.726 0.950 1.583 1.100 0.861 0.864
Trade/GDP 0.035 -0.006 -0.008 -0.014 -0.019 -0.015 42
1.656 0.497 0.364 0.441 0.482 0.423
ln(1+Inflation) -0.002 0.003 -0.005 -0.023 -0.038 -0.031 39
0.073 0.258 0.196 0.681 0.896 0.778
Government -0.110 -0.033 -0.225 -0.278 -0.327 -0.327 42
Consumption/GDP 0.742 0.421 1.526 1.350 1.256 1.357
M2/GDP -0.028 -0.016 -0.053 -0.048 -0.045 -0.045 42
0.663 0.722 1.205 0.783 0.572 0.618
KK Voice and 0.021 0.008 0.013 0.011 0.101 0.009 42
Accountability 1.659 1.231 1.049 0.646 0.462 0.438
Relative Productivity 0.013 0.021 0.044 0.050 0.054 0.056 42
in Agriculture 0.543 1.388 1.513 1.277 1.105 1.217
Average Years of -0.001 0.008 0.004 0.006 0.008 0.006 32
Primary Education 0.063 2.601 0.638 0.521 0.558 0.475
Note: Heteroskedasticity-consistent t-statistics reported below coefficient estimates.
34
Table 6: Multivariate Growth and Distributional Change Regressions
Annual Percent Change in: Annual Percent Change in Headcount Annual Percent Change in Watts Index
Survey Mean Gini Growth Distribution Growth Distribution
Component Component Component Component
Initial Income -0.017 -0.008 0.037 -0.018 0.040 -0.023
1.978 1.470 1.769 1.409 1.577 1.284
KK Rule of Law 0.022 0.009 -0.046 0.029 -0.049 0.029
2.050 0.919 2.188 1.785 2.108 1.221
Trade/GDP 0.045 -0.011 -0.060 -0.007 -0.077 -0.011
1.978 0.911 1.659 0.361 1.667 0.310
Government -0.280 -0.048 0.600 -0.244 0.667 -0.330
Consumption 2.229 0.425 2.114 1.010 2.124 1.175
R-Squared 0.239 0.095 0.263 0.182 0.222 0.103
# Observations 42 42 42 42 42 42
Note: Heteroskedasticity-consistent t-statistics reported below coefficient estimates.
35
Figure 1: Relative Growth Incidence Curves
0.08
0.06
0.04 Indonesia 1996-1999
p
0.02
0
Percentile Average 0 0.2 0.4 0.6 0.8 1
of to -0.02
China 1990-1998
Rate -0.04
Relative
-0.06
Growth
-0.08
-0.1
-0.12 Percentile of Income Distribution, p
36
Figure 2: Sensitivity of Poverty to Growth in Percentile p
Percentile of Income Distribution, p
0
0 0.2 0.4 P(2) 0.6 0.8 1
-0.2
EDEI(-1)
-0.4
p
-0.6
Measure P(1)
-0.8
Percentile
Poverty in -1
of Watts
-1.2
Growth -1.4
to P(0)
Sensitivity -1.6
-1.8
-2
37
Figure 3: Growth and Poverty Reduction: Long Spells
ETH9500
Headcount Poverty Gap
0.2 PER8596 0.2
ETH9500 PER8596
0.15 0.15
0.1 YEM9298 0.1 PRY9098
YEM9298 BDI9298
Poverty CIV8595 Poverty
in 0.05COL8898 in CIV85950.05COL8898BOL9099
PRY9098
GHA8799
RUS9398
BDI9298BOL9099
VEN8198ZMB9198
BGD8400 LSO8695
GHA8799 VEN8198ZMB9198
BGD8400
NGA8597
Growth 0 MDG8099
NGA8597 0RUS9398
IND8397 LSO8695
BWA8593 Growth MDG8099
IND8397 BWA8593
-0.15 -0.1 -0.05 0 -0.15 -0.1 -0.05 0 0.05 0.1 0.15
EGY9199SLV8998
LKA85950.05 0.1 0.15 LKA8595
TUN8590
TUN8590
MYS8497
-0.05 ECU8895CRI8696
MRT8895 -0.05 MYS8497 CRI8696
Annual CHN9098 EGY9199SLV8998
CHN9098
HND8998
y = -1.16x - 0.01 JAM8800MEX8998 ECU8895
THA8800
UGA8996
THA8800HND8998 Annual
MEX8998
GTM8700
R2 = 0.51 -0.1KEN9297PAN9197
IDN8700
BRA8598PHL8500 -0.1 JAM8800
MRT8895 GTM8700
y = -1.44x - 0.01 BRA8598PHL8500
IDN8700
Average PAK8798 GMB9298 Average R2 = 0.45 UGA8996
AZE9501
-0.15 -0.15KEN9297PAN9197 GMB9298
PAK8798
AZE9501
-0.2 -0.2
Average Annual Growth in Survey Mean Average Annual Growth in Survey Mean
VNM9398 VNM9398
ETH9500 ETH9500
Squared Poverty Gap Watts
PER8596 0.2 PER8596 0.2
0.15 0.15
BDI9298 PRY9098 PRY9098
YEM9298 0.1 YEM9298 0.1BDI9298
Poverty BOL9099 Poverty
COL8898 COL8898BOL9099
in in CIV8595
0.05
CIV8595
0.05 GHA8799
LSO8695 GHA8799 LSO8695
BGD8400
VEN8198ZMB9198
BGD8400
NGA8597 VEN8198 NGA8597
Growth 0 0RUS9398
MDG8099
ZMB9198
MDG8099 BWA8593 Growth BWA8593
RUS9398TUN8590
IND8397 IND8397
-0.15 -0.1 -0.05 0 -0.15 -0.1 -0.05 0 LKA8595
TUN8590
0.05 0.1 0.15
LKA85950.05 0.1 0.15
HND8998
-0.05 MYS8497 CRI8696
HND8998 -0.05
EGY9199SLV8998
MYS8497 CRI8696
CHN9098
Annual CHN9098 Annual
EGY9199THA8800
SLV8998 THA8800
MEX8998 ECU8895
MEX8998
-0.1 ECU8895 -0.1 JAM8800 PHL8500
GTM8700
JAM8800 PHL8500
GTM8700 y = -1.52x - 0.01 IDN8700
BRA8598
Average BRA8598 Average
y = -1.63x - 0.01 MRT8895
IDN8700 GMB9298
MRT8895 GMB9298 R2 = 0.41 -0.15
R2 = 0.40 -0.15 UGA8996
PAN9197 KEN9297PAN9197
UGA8996 PAK8798
KEN9297 AZE9501
-0.2 PAK8798 -0.2
AZE9501
Average Annual Growth in Survey Mean Average Annual Growth in Survey Mean
VNM9398
VNM9398
38
Figure 4: Decomposing Changes in the Headcount
Growth and Distribution Components vs Residual
0.25
0.2 ETH9500
PER8596
y = 0.97x - 0.00 0.15
and R2 = 0.92 0.1
YEM9298
COL8898
Components 0.05 BDI9298 CIV8595
Growth MDG8099
NGA8597
VEN8198
GHA8799
ZMB9198
BGD8400
RUS9398
BOL9099
of -0.2 -0.15 -0.1 UGA8996
-0.05TUN8590BWA85930.05
MRT8895IND8397
CRI8696LSO8695
SLKA8595
EGY9199
0V8998
ECU8895
-0.05 0
MYS8497 PRY90980.1 0.15 0.2
PAK8798GTM8700
IDN8700HND8998
BRA8598 CHN9098
JAM8800
MEX8998
THA8800
AZE9501 KEN9297
PAN9197 -0.1
Sum
Distribution GMB9298
PHL8500 -0.15
VNM9398 -0.2
Average Annual Growth in Headcount
Growth Component vs Distribution Component
YEM9298
y = 0.92x + 0.00 0.2
ETH9500
R2 = 0.70 0.15 PER8596
0.1 CIV8595
0.05 VEN8198
KEN9297 MDG8099BDI9298
RUS9398COL8898
Component
-0.2 -0.15 -0.1
PAN9197UGA8996MRT8895
BRA8598
JAM8800MYS8497NGA8597
EGY9199ZMB9198
0 BGD8400
-0.05TUN8590
-0.05SLV8998
ECU8895
LKA8595 BOL9099
IND8397
GHA8799
0 0.05 0.1 0.15 0.2
AZE9501
IDN8700
HND8998
MEX8998 LSO8695
BWA8593
CRI8696
GTM8700
THA8800
CHN9098
PAK8798 -0.1
Growth -0.15
GMB9298
PHL8500 PRY9098
VNM9398 -0.2
Sum of Growth and Distribution Components
Growth in Mean vs Sensitivity of Poverty to Growth
GMB9298
0.1
PRY9098 GTM8700
VNM9398 PHL8500 LSO8695
HND8998
CHN9098BWA8593
Mean PAK8798 CRI8696 0.05
MEX8998
THAZE9501
8800 BRA8598
ECU8895
MRT8895
SLV8998
IDN8700 TUN8590 NGA8597
UGA8996
LKA8595EGY9199
JAM8800
BOL9099
MYS8497
GHA8799
0IND8397
PAN9197
BGD8400
Survey ZMB9198
MDG8099
COL8898
BDI9298
RUS9398
KEN9297
in -0.15 -0.1 -0.05 0 0.05 0.1 0.15
VEN8198
-0.05 CIV8595
Growth PER8596
y = -0.52x + 0.00 -0.1 YEM9298
R2 = 0.88 Growth Component
ETH9500
39
Figure 5: Policies and the Growth and Distribution Components
of Changes in Policy
6%
t
4%
Headcoun
in
2%
Change
0%
Net Net Net
Percent
-2% Growth Growth Growth
Annual Distribution Distribution Distribution
-4%
Average Rule of Gov/GDP
Law Trade/GDP
-6%
40